Vehicle state estimation apparatus and method

ABSTRACT

The present disclosure relates to an apparatus ( 1 ) for estimation of a vehicle state. The apparatus ( 1 ) includes a controller ( 21 ) configured to determine a first estimation of the vehicle state in dependence on at least one first vehicle dynamics parameter. A filter coefficient (F C ) is calculated based on a first vehicle operating parameter. An operating frequency of a first signal filter ( 35 ) is set in dependence on the determined filter coefficient (F C ) and the first estimation is filtered to generate a first filtered estimation of the vehicle state. The present disclosure also relates to a vehicle; and to a method of estimating a vehicle state.

RELATED APPLICATIONS

This application is a 35 U.S.C. § 371 national stage application of PCTApplication No. PCT/EP2016/053421, filed on Feb. 18, 2016, which claimspriority from Great Britain Patent Application No. 1503552.0 filed onMar. 3, 2015, the contents of which are incorporated herein by referencein their entireties. The above-referenced PCT International Applicationwas published in the English language as International Publication No.WO 2016/139068 A2 on Sep. 9, 2016.

TECHNICAL FIELD

The present disclosure relates to apparatus for estimation of a vehiclestate; to a vehicle incorporating vehicle state estimation apparatus; toa dynamic filtering apparatus; to a method of estimating a vehiclestate; and to a method of performing dynamic filtering.

BACKGROUND

The instantaneous state of a vehicle is defined by state parameters forvehicle pitch, vehicle roll and vehicle yaw. The vehicle state changescontinuously while the vehicle is in motion, for example due toacceleration/deceleration of the vehicle and changes in the gradient ofthe surface on which the vehicle is travelling. The vehicle state isused by on-board vehicle dynamic control systems, for example to controlvehicle stability.

In the automotive field it is known to employ an inertial monitoringunit (IMU) to continuously monitor vehicle acceleration in six degreesof freedom to monitor the vehicle state. It would be desirable to obtainmore reliable data from the IMU and potentially to simplify the IMU byreducing the number of degrees of freedom in which acceleration andrates are measured. One approach is to estimate the vehicle state usingmeasured dynamic parameters. However, global state estimationscalculated from vehicle sensors are susceptible to noise and, in certainconditions, high error levels. Signal filtering can be used to lessenundershoots and overshoots in these conditions. However, for largesignal-to-noise ratios heavy filtering is required which result in poorestimations in transient conditions.

It is against this backdrop that the present invention(s) has beenconceived. At least in certain embodiments, the present invention seeksto overcome or ameliorate some of the shortcomings associated with knownvehicle state estimation systems.

SUMMARY OF THE INVENTION

Aspects of the present invention relate to apparatus for estimation of avehicle state; to a vehicle incorporating vehicle state estimationapparatus; to a dynamic filtering apparatus; to a method of estimating avehicle state; and to a method of performing dynamic filtering.

According to a further aspect of the present invention there is providedapparatus for estimation of a vehicle state, the apparatus comprising acontroller configured to:

-   -   determine a first estimation of the vehicle state in dependence        on at least one first vehicle dynamics parameter;    -   determine a filter coefficient in dependence on a first vehicle        operating parameter;    -   set an operating frequency of a first signal filter in        dependence on the determined filter coefficient and use the        first signal filter to filter the first estimation to generate a        first filtered estimation of the vehicle state; and    -   output a control signal in dependence on the first filtered        estimation of the vehicle state. In use, the operating frequency        of the first signal filter can be adjusted dynamically. By        dynamically changing the operating frequency, an improved        vehicle state estimation can be provided at least in certain        embodiments. By identifying when large signal-to-noise ratios        will occur, a strategy can be implemented based on the first        vehicle operating parameter which utilizes a continuously        varying filter. At least in certain embodiments, the signal        filter can be controlled to capture transient vehicle states        while removing erroneous information. The first filtered        estimation can be output as a first filtered estimation signal        for use by a vehicle stability control system.

The at least one first vehicle dynamics parameter can each be measured,for example by one or more vehicle sensors. The controller can beconfigured to receive a vehicle dynamics signal from each sensor. The atleast one first vehicle dynamics parameter can be one or more of thefollowing set: reference velocity, longitudinal velocity, longitudinalacceleration, lateral velocity, lateral acceleration, vertical velocity,vertical acceleration, roll, yaw, pitch and wheel slip.

The first estimation of the vehicle state can be generated using anappropriate vehicle state estimation algorithm. The vehicle state couldbe a roll angle of the vehicle measured about a longitudinal axis of thevehicle, which could be estimated using the lateral acceleration and/orthe lateral velocity of the vehicle. Alternatively, the vehicle statecan be a pitch angle of the vehicle and the at least one first vehicledynamics parameter can include a reference velocity along a longitudinalaxis of the vehicle. The pitch angle can be a global pitch angle of thevehicle. The global pitch angle is the angle of a longitudinal axis ofthe vehicle body relative to a horizontal reference plane. The globalpitch angle can be calculated using the following global pitchestimation algorithm:

$\theta_{y} = {\sin^{- 1}( \frac{a_{x} - \overset{.}{u} + {\omega_{z} \cdot v_{y}}}{g} )}$Where: θ_(y) is the global pitch angle;

-   -   a_(x) is the measured longitudinal acceleration;    -   {dot over (u)} is the derivative of the reference velocity U;    -   ω_(z) is the angular velocity about the Z axis;    -   v_(y) is the estimated lateral velocity; and    -   g is the acceleration due to gravity.

The first signal filter can be a low-pass signal filter and theoperating frequency can be a cut-off frequency of the low-pass signalfilter. The low-pass signal filter passes signals having a frequencylower than the cut-off frequency.

The controller can be configured to determine a second estimation of thevehicle state in dependence on at least one second vehicle dynamicsparameter. An operating frequency of a second signal filter can be setin dependence on the determined filter coefficient. The controller canbe configured to use the second signal filter to filter the secondestimation to generate a second filtered estimation of the vehiclestate.

The second estimation can be a relative body pitch angle of the vehicle.The relative body pitch angle is the pitch angle of the vehicle body inrelation to a reference road axis determined by the gradient of the roadon which the vehicle is travelling. The relative body pitch anglechanges due to dynamic loads (such as acceleration/deceleration forces)compared to a static condition. The relative body pitch angle may alsochange to vehicle loads. The second estimation could be calculated basedon the at least one second vehicle dynamics parameter. Alternatively,the second estimation can be determined by referencing the at least onesecond vehicle dynamics parameter to a look-up table stored in systemmemory. The at least one second vehicle dynamics parameter can include alongitudinal acceleration of the vehicle.

The second signal filter can be a high-pass signal filter, and theoperating frequency can be a cut-off frequency of the high-pass signalfilter. The high-pass signal filter passes signals having a frequencyhigher than the cut-off frequency.

The cut-off frequency of the low-pass signal filter can be set the sameas the cut-off frequency of the high-pass signal filter. The low-passsignal filter and the high-pass filter thereby provide complementaryfiltering of said first and second estimations. By way of example, thecut-off frequency can be set in the range 0 to 1 Hertz; or in the range0 to 0.7 Hertz.

The controller can be configured to combine the first and secondfiltered estimations to generate an output signal. For example, thecontroller can sum the first and second filtered estimations.

The first vehicle operating parameter can comprise a vehicle dynamicsparameter or a vehicle control input. The first vehicle operatingparameter could be a vehicle dynamics parameter which is either the sameas or different from the at least one first vehicle dynamics parameter.Alternatively, the first vehicle operating parameter can be a vehiclecontrol input, such as a throttle pedal position or a brake pressure.

The controller can generate a confidence value of the first estimationin dependence on the first vehicle operating parameter. The firstconfidence value can be generated in dependence on an absolute value ofthe first vehicle operating parameter. The first confidence value can beproportional (either directly or inversely) to the first vehicleoperating parameter. The first confidence value can be generated independence on the rate of change of the first vehicle operatingparameter. The first confidence value can be proportional (eitherdirectly or inversely) to the rate of change of the first vehicleoperating parameter. A filter can be applied to the determined rate ofchange of the first vehicle operating parameter. The filter coefficientcan be calculated based on said confidence value. The confidence valueprovides an indication of the confidence that the first estimation ofthe vehicle state is accurate.

The first vehicle operating parameter can comprise longitudinal vehicleacceleration. The controller can be configured to determine a rate ofchange of the longitudinal vehicle acceleration to generate the firstconfidence value of the first estimation. A high frequency filter can beapplied to the determined rate of change of the longitudinal vehicleacceleration.

The first vehicle operating parameter can comprise a throttle pedalposition. The controller can be configured to determine a rate of changeof the throttle pedal position to generate a second confidence value ofthe first estimation. The controller can be configured to apply a highfrequency filter to the determined rate of change of the throttle pedalposition. The throttle pedal position generates a torque request signalfor controlling operation of an internal combustion engine and/or anelectric traction machine. In certain embodiments, the torque requestsignal would be equivalent to the throttle pedal position.

The first vehicle operating parameter can comprise brake pressure. Thecontroller can be configured to analyse the brake pressure to generate athird confidence value of the first estimation. In certain embodiments,the position of the brake pedal would be equivalent to the brakepressure.

The first vehicle operating parameter can comprise at least one wheelslip measurement; and the controller can be configured to analyse the atleast one wheel slip measurement to generate a fourth confidence valueof the first estimation. The wheel slip measurement can be determinedbetween wheels on the same side of the vehicle. Alternatively, the wheelslip measurement can be determined for laterally opposed wheels ordiametrically opposed wheels. The at least one wheel slip measurementcan be compared to a look-up table to generate the fourth confidencevalue. The analysis of the wheel slip measurement can comprise comparingfirst and second wheel slip measurements to a look-up table.

The controller can generate one or more confidence values based ondifferent vehicle dynamic parameters and/or vehicle control inputs. Thecontroller can be configured to normalize the one or more confidencevalues. The one or more confidence values can be normalized to fallwithin a predetermined range, for example a range of zero (0) to one (1)inclusive. A linear or non-linear gain can be applied to normalize eachconfidence value.

The controller can be configured to determine the filter coefficient independence on the generated confidence value. The controller can beconfigured to generate a plurality of said confidence values. Theconfidence values can each be generated in dependence on a differentfirst operating parameter. The controller can be configured to generatethe filter coefficient in dependence on the generated confidence valueindicating the lowest confidence in the accuracy of the firstestimation.

The controller can be configured to invert the generated confidencevalue. For example, the controller can subtract each confidence valuefrom one (1). The filter coefficient can be generated in dependence onthe inverted confidence value.

According to a further aspect of the present invention there is provideda dynamic filtering apparatus comprising a controller configured to:

-   -   generate a first signal and a second signal;    -   calculate a cut-off frequency;    -   apply the calculated cut-off frequency to a low-pass signal        filter and filter the first signal using the low-pass signal        filter;    -   apply the calculated cut-off frequency to a high-pass signal        filter and filter the second signal using the high-pass signal        filter; and    -   combine the filtered outputs of said low-pass signal filter and        said high-pass signal filter. The low-pass signal filter and the        high-pass filter thereby provide complementary filtering of the        first and second signals. The controller can be configured to        output a control signal in dependence on the combined filtered        outputs of said low-pass signal filter and said high-pass signal        filter.

The controller can be configured to generate the first signal independence on at least one first parameter. The at least one firstparameter can be at least one operating parameter of a vehicle. The atleast one operating parameter can be at least one first vehicle dynamicsparameter, such as one or more of the following set: reference velocity,longitudinal velocity, longitudinal acceleration, lateral velocity,lateral acceleration, vertical velocity, vertical acceleration, roll,yaw, pitch and wheel slip.

The controller can be configured to generate the second signal independence on at least one second parameter. The at least one secondparameter can be at least one operating parameter of a vehicle. The atleast one operating parameter of the vehicle can be at least one secondvehicle dynamics parameter, such as one or more of the following set:reference velocity, longitudinal velocity, longitudinal acceleration,lateral velocity, lateral acceleration, vertical velocity, verticalacceleration, roll, yaw, pitch, and wheel slip. The first and secondvehicle dynamics parameters can be the same as each other or differentfrom each other.

The controller can be configured to calculate a first confidence valueof the first signal. The cut-off frequency can be calculated independence on said first confidence value. The controller can beconfigured to calculate the first confidence value in dependence on athird parameter. The third parameter can be an operating parameter of avehicle. The operating parameter could be one of said vehicle dynamicsparameters, which is either the same as or different from the at leastone first vehicle dynamics parameter. Alternatively, the third parametercan be a vehicle control input, such as a throttle pedal position or abrake pressure.

The controller can be configured to calculate an additional confidencevalue of the first signal. The additional confidence value can becalculated in dependence on a fourth operating parameter of the vehicle.The fourth operating parameter could be one of said vehicle dynamicsparameters, which is either the same as or different from the secondvehicle dynamics parameter. Alternatively, the fourth operatingparameter can be a vehicle control input, such as a throttle pedalposition or a brake pressure.

The controller could be configured to calculate a second confidencevalue of the second signal. The first and second confidence values couldbe combined, for example by applying a weighting. The combinedconfidence value could be used to determine the cut-off frequency of thelow-pass signal filter and the cut-off frequency of the high-pass signalfilter.

According to a further aspect of the present invention there is provideda vehicle incorporating the apparatus described herein.

According to a further aspect of the present invention there is provideda method of estimating a vehicle state, the method comprising:

-   -   determining a first estimation of the vehicle state in        dependence on at least one first vehicle dynamics parameter;    -   determining a filter coefficient in dependence on a first        vehicle operating parameter;    -   setting an operating frequency of a first signal filter in        dependence on the determined filter coefficient and using the        first signal filter to filter the first estimation to generate a        first filtered estimation of the vehicle state; and    -   outputting a control signal in dependence on the first filtered        estimation of the vehicle state.

The vehicle state can be a pitch angle of the vehicle measured about atransverse axis. The at least one first vehicle dynamics parameter cancomprise a reference velocity along a longitudinal axis of the vehicle.

The first signal filter can be a low-pass signal filter. The operatingfrequency of the first signal filter can be a cut-off frequency of thelow-pass signal filter.

The method can comprise: determining a second estimation of the vehiclestate in dependence on at least one second vehicle dynamics parameter;setting an operating frequency of a second signal filter in dependenceon the determined filter coefficient and using the second signal filterto filter the second estimation to generate a second filtered estimationof the vehicle state. The second estimation can be determined byreferencing the at least one second vehicle dynamics parameter to alook-up table stored in system memory.

The second signal filter can be a high-pass signal filter. The operatingfrequency of the second signal filter can be a cut-off frequency of thehigh-pass signal filter.

The method can comprise combining the first and second filteredestimations.

The second estimation of the vehicle state can determine a relative bodypitch angle of the vehicle.

The method can comprise generating a confidence value of the firstestimation in dependence on a first vehicle operating parameter. Thefirst confidence value can be generated in dependence on an absolutevalue of the first vehicle operating parameter. The first confidencevalue can be proportional (either directly or inversely) to the firstvehicle operating parameter. The first confidence value can be generatedin dependence on the rate of change of the first vehicle operatingparameter. The first confidence value can be proportional (eitherdirectly or inversely) to the rate of change of the first vehicleoperating parameter. A filter can be applied to the determined rate ofchange of the first vehicle operating parameter. The filter coefficientcan be calculated based on said confidence value.

The first vehicle operating parameter can comprise longitudinal vehicleacceleration. The method can comprise determining a rate of change ofthe longitudinal vehicle acceleration. The first confidence value of thefirst estimation can be generated in dependence on said determined rateof change of the longitudinal vehicle acceleration. A high frequencyfilter can be applied to the determined rate of change of thelongitudinal vehicle acceleration.

The first vehicle operating parameter can comprise a throttle pedalposition. The method can comprise determining a rate of change of thethrottle pedal position to generate a second confidence value of thefirst estimation. A high frequency filter can be applied to thedetermined rate of change of the throttle pedal position.

The first vehicle operating parameter can comprise brake pressure. Themethod can comprise analysing the brake pressure to generate a thirdconfidence value of the first estimation.

The first vehicle operating parameter can comprise at least one wheelslip measurement. The method can comprise analysing the at least onewheel slip measurement to generate a fourth confidence value of thefirst estimation. The at least one wheel slip measurement can becompared to a look-up table to generate the fourth confidence value. Theanalysis of the wheel slip measurement can comprise comparing first andsecond wheel slip measurements to a look-up table.

A filter coefficient can be generated in dependence on the generatedconfidence value, or in dependence on one of the generated confidencevalues. The filter coefficient can be generated in dependence on thegenerated confidence value indicating the lowest confidence in theaccuracy of the first estimation. The method can comprise inverting thegenerated confidence value, the filter coefficient being generated independence on the inverted confidence value.

According to a further aspect of the present invention there is provideda dynamic filtering method comprising:

-   -   generating a first signal and a second signal;    -   calculating a cut-off frequency;    -   applying the calculated cut-off frequency to a low-pass signal        filter and filtering the first signal using the low-pass signal        filter;    -   applying the calculated cut-off frequency to a high-pass signal        filter and filtering the second signal using the high-pass        signal filter; and    -   combining the filtered outputs of said low-pass signal filter        and said high-pass signal filter. The cut-off frequency of the        low-pass signal filter and the cut-off frequency of the        high-pass signal filter are set at the same frequency to provide        complementary filtering of the first and second signals. The        method can comprise outputting a control signal in dependence on        the combined filtered outputs of said low-pass signal filter and        said high-pass signal filter.

The first signal can be generated in dependence on a first parameter.The second signal can be generated in dependence on a second parameter.

The method can comprise calculating a first confidence value of thefirst signal. The cut-off frequency can be calculated in dependence onsaid first confidence value. The first confidence value can becalculated in dependence on a third parameter.

According to a further aspect of the present invention there is provideda controller configured to perform the method(s) described herein. Thecontroller can be configured to perform a set of computationalinstructions held in system memory. When executed, the computationalinstructions can cause the controller to perform the method(s) describedherein. The controller can be a general purpose computational device orcan be a dedicated computational device.

According to a further aspect of the present invention there is provideda machine-readable medium containing a set of computational instructionswhich, when executed, cause a controller to perform the method(s)described herein.

Any controller or controllers described herein may suitably comprise acontrol unit or computational device having one or more electronicprocessors. Thus the system may comprise a single control unit orelectronic controller or alternatively different functions of thecontroller may be embodied in, or hosted in, different control units orcontrollers. As used herein the term “controller” or “control unit” willbe understood to include both a single control unit or controller and aplurality of control units or controllers collectively operating toprovide any stated control functionality. To configure a controller, asuitable set of instructions may be provided which, when executed, causesaid control unit or computational device to implement the controltechniques specified herein. The set of instructions may suitably beembedded in said one or more electronic processors. Alternatively, theset of instructions may be provided as software saved on one or morememory associated with said controller to be executed on saidcomputational device. A first controller may be implemented in softwarerun on one or more processors. One or more other controllers may beimplemented in software run on one or more processors, optionally thesame one or more processors as the first controller. Other suitablearrangements may also be used.

Within the scope of this application it is expressly intended that thevarious aspects, embodiments, examples and alternatives set out in thepreceding paragraphs, in the claims and/or in the following descriptionand drawings, and in particular the individual features thereof, may betaken independently or in any combination. That is, all embodimentsand/or features of any embodiment can be combined in any way and/orcombination, unless such features are incompatible. The applicantreserves the right to change any originally filed claim or file any newclaim accordingly, including the right to amend any originally filedclaim to depend from and/or incorporate any feature of any other claimalthough not originally claimed in that manner.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments of the present invention will now be described,by way of example only, with reference to the accompanying drawings, inwhich:

FIG. 1 shows a schematic overview of a vehicle incorporating a vehiclestate estimation apparatus in accordance with an embodiment of thepresent invention;

FIG. 2 illustrates the pitch angles of a vehicle travelling on aninclined surface;

FIG. 3 represents the measurements taken by an inertial measurement unitprovided on a vehicle;

FIG. 4 shows a flow diagram representing the operation of the globalpitch angle estimator shown in FIG. 3;

FIG. 5 shows a schematic representation of the processor functions forthe vehicle state estimation apparatus;

FIG. 6 illustrates the application of a variable low-pass signal filterand a variable high-pass signal filter to the unfiltered global pitchangle and the relative body pitch angle;

FIG. 7 illustrates the generation of a filter coefficient to control thevariable low-pass signal filter and the variable high-pass signal filtershown in FIG. 6;

FIG. 8 shows a block diagram representing the operation of the processorto generate confidence values to determine the filter coefficient;

FIGS. 9A, B and C show a first set of measured vehicle parameters withrespect to time;

FIGS. 10A and 10B show the dynamic filtering of pitch estimation basedon the measured vehicle parameters shown in FIGS. 9A-C;

FIGS. 11A, B and C show a second set of measured vehicle parameters withrespect to time;

FIGS. 12A and 12B show the dynamic filtering of pitch estimation basedon the measured vehicle parameters shown in FIGS. 11A-C

FIG. 13 shows a schematic overview of a vehicle incorporating a vehiclereference velocity estimation apparatus in accordance with a furtherembodiment of the present invention;

FIG. 14 shows a schematic representing of the reference velocity for thevehicle shown in FIG. 13;

FIG. 15 shows a flow diagram representing determination of thelongitudinal vehicle velocity estimate;

FIG. 16 shows a flow diagram representing determination of the referencevelocity confidence estimator and output to a vehicle dynamiccontroller;

FIG. 17 shows a flow diagram representing dynamic filtering of thereference velocity in dependence on control inputs;

FIG. 18 shows a flow diagram representing dynamic high-pass and low-passfiltering of the vehicle reference velocity;

FIG. 19 shows a flow diagram representing operation of a side slipproportional derivative controller in dependence on a determinedreference velocity confidence.

DETAILED DESCRIPTION

A vehicle state estimation apparatus 1 in the form of a global pitchangle estimator will now be described with reference to the accompanyingFigures.

A schematic representation of a vehicle 3 incorporating the vehiclestate estimation apparatus 1 is shown in FIG. 1. The vehicle 3 in thepresent embodiment is an automotive vehicle having four wheels FL, FR,RL, RR, an inertial measurement unit (IMU) 5, a throttle pedal 7, abrake pedal 9 and a steering wheel 11. A rotational speed sensor 13 isprovided to measure the rotational speed of each wheel FL, FR, RL, RR togenerate wheel speed signals WS1-4. The wheel speed signals WS1-4 areused to determine a reference velocity V of the vehicle 3 and, asdescribed herein, to detect wheel spin. A first position sensor 15 isprovided to measure the position of the throttle pedal 7 and to output athrottle pedal position signal S1. A pressure sensor 17 is provided tomeasure the hydraulic pressure in the brake system and to output a brakepressure signal S2. A steering wheel angle sensor 19 is provided tomeasure the steering angle θ of the steering wheel 11 and to output asteering angle signal S3.

The vehicle state is defined with reference to a longitudinal axis X, atransverse axis Y and a vertical axis Z of the vehicle 3. The referencespeed V of the vehicle 3 is measured along the longitudinal axis X. Asshown in FIG. 2, rotation about the longitudinal axis X is referred toas vehicle roll; rotation about the transverse axis Y is referred to asvehicle pitch; and rotation about the vertical axis Z is referred asvehicle yaw. The attitude of the vehicle 3 is defined by a roll angleθ_(x) (angular rotation about the longitudinal axis X), a pitch angleθ_(y) (angular rotation about the transverse axis Y) and a yaw angleθ_(z) (angular rotation about the vertical axis Z). The IMU 5 comprisesaccelerometers arranged to measure acceleration in six degrees offreedom, as illustrated in FIG. 3. The IMU 5 comprises an accelerometeradapted to measure the longitudinal acceleration A_(X) of the vehicle 3(i.e. acceleration along the longitudinal axis X) and to output alongitudinal acceleration signal. Suitable IMUs are known in the art anddescribed, for example IMU BMI055 produced by Bosch-Sensortec whichmeasures six degrees of freedom.

The pitch angle θ_(y) of the vehicle 3 in relation to a horizontal axisand is referred to as the global pitch angle θ_(y). The global pitchangle θ_(Y) comprises a road pitch angle θ_(Y1) and a relative bodypitch angle θ_(Y2). The road pitch angle θ_(Y1) corresponds to anincline angle of the road (or other surface on which the vehicle 3 issituated); and the relative body pitch angle θ_(Y2) corresponds to thepitch of the vehicle body relative to the road pitch angle θ_(Y1). Therelative body pitch angle θ_(Y2) changes due toacceleration/deceleration forces and/or vehicle loads. The global pitchangle θ_(y) is used to estimate lateral kinematics and velocities, forexample to determine a side slip angle of the vehicle 3.

As shown in FIG. 1, the vehicle state estimation apparatus 1 comprises aprocessor 21 coupled to system memory 23. The processor 21 is configuredto perform a set of computational instructions held in the system memory23. The processor 21 is in communication with a vehicle communicationnetwork, such as a controller area network (CAN) bus or FlexRay, toreceive the wheel speed signals WS1-4, the longitudinal accelerationsignal, the throttle pedal position signal S1, the brake pressure signalS2 and the steering angle signal S3.

The longitudinal acceleration signal output by the IMU 5 contains acomponent due to gravity and, under yaw conditions, a component fromcentripetal acceleration. These components may contaminate thelongitudinal acceleration signal and result in errors. In order todetermine the global pitch angle θ_(y) the vehicle pure longitudinalacceleration is determined from the reference velocity V. The referencevelocity V is calculated from the wheel speed signals WS1-4, either bythe processor 21 or a separate processor. In the present embodiment, thereference velocity V is calculated as the mean of the rotational speedsof the wheels FL, FR, RL, RR, however any other known methods ofobtaining a reference velocity, for example the speed of the secondslowest moving wheel or the average speed of two un-driven wheels of thevehicle, may of course be used. As will be understood the term referencevelocity is a term used in the art to describe a speed of a vehiclederived from the speeds of two or more individual wheels speeds. Usingthe assumption that the vehicle 3 is in a condition of linear side slip,the estimated lateral velocity at the rear of the vehicle 3 can betranslated to the position of the IMU 5. This assumption allows theglobal pitch angle θ_(y) to be calculated using the following globalpitch estimation algorithm:

$\theta_{y} = {\sin^{- 1}( \frac{a_{x} - \overset{.}{u} + {\omega_{z} \cdot v_{y}}}{g} )}$Where: θ_(y) is the global pitch angle;

-   -   a_(x) is the measured longitudinal acceleration;    -   {dot over (u)} is the derivative of the reference velocity V;    -   ω_(z) is the angular velocity about the Z axis;    -   v_(y) is the estimated lateral velocity; and    -   g is the acceleration due to gravity.

An overview of the operation of the vehicle state estimation apparatus 1is provided in a first flow diagram 100 shown in FIG. 4. The vehiclestate estimation apparatus 1 receives the measured longitudinalacceleration A_(X) and the reference velocity V (STEP 105). A firstestimation of the global pitch angle θ_(y) is calculated (STEP 110)using the global pitch estimation algorithm. A variable frequency filteris applied to the calculated (raw) global pitch angle θ_(y) to removenoise or erroneous overshoots. An operating frequency of the variablefrequency filter is determined based on a confidence value providing anindication of the confidence in the calculated global pitch angle θ_(y).A plurality of confidence values are calculated in dependence on atleast one vehicle dynamic parameter and/or at least one control inputwhich one of the confidence values is selected to determine a filtercoefficient to control an operating frequency of the variable frequencyfilter. In the present embodiment, the at least one vehicle dynamicparameter comprises wheel spin which influences the accuracy of thedetermined reference velocity V; and the at least one control inputreceived by the vehicle state estimation apparatus 1 comprises thethrottle pedal position signal S1 and/or the brake pressure signal S2(STEP 115). A check is performed to determine if the control input(s) issignificant (STEP 120), for example to determine if a detected rate ofchange of the control input is greater than a defined threshold. If thevehicle state estimation apparatus 1 determines that the control inputsare not significant, no action is required (STEP 125). If the vehiclestate estimation apparatus 1 determines that the control inputs aresignificant, the confidence value is modified dynamically. Thedetermined confidence value sets the operating frequency of the variablefrequency filter which is then used to filter the calculated globalpitch angle θ_(y) (STEP 130). The filtered global pitch signal is outputas a filtered global pitch angle estimate θ_(YF) (STEP 135) which can beused, for example, to implement vehicle dynamics control.

The processor 21 is represented schematically in FIGS. 5, 6 and 7. Theprocessor 21 is configured to implement a reference velocity calculator25, a global pitch calculator 27, a relative body pitch calculator 29, aslip calculator 31 and a confidence estimator 33. The processor 21 alsoimplements a variable frequency low-pass signal filter 35 and a variablefrequency high-pass signal filter 37. The cut-off frequency of thevariable frequency low-pass signal filter 35 can be set at between zero(0) and one (1) Hertz inclusive. The cut-off frequency of the variablefrequency high-pass signal filter 37 can be set between zero (0) and one(1) Hertz, inclusive. The cut-off frequency of the variable frequencylow-pass signal filter 35 is set at the same value as the cut-offfrequency of the variable frequency high-pass signal filter 37. It willbe understood that the filter range could be changed for differentapplications. For example, a filter range of 0-0.7 Hz would be workable.

The reference velocity calculator 25 receives the wheel speed signalsWS1-4 from the rotational speed sensors 13 associated with each wheelFL, FR, RL, RR. The reference velocity V is calculated using the wheelspeed signals WS1-4 to determine the mean rotational speed WS of thewheels FL, FR, RL, RR. The reference velocity V is output to the globalpitch calculator 27 and to the slip calculator 31. As outlined above,the global pitch calculator 27 uses the reference velocity V and themeasured longitudinal acceleration A_(X) to calculate the global pitchangle θ_(y). The global pitch angle θ_(y) is output to the variablefrequency low-pass signal filter 35. The relative body pitch calculator29 uses the measured longitudinal acceleration A_(X) to determine therelative body pitch angle θ_(Y2). In particular, the relative body pitchcalculator 29 cross-references the measured longitudinal accelerationA_(X) with empirically derived data stored in the system memory 23 todetermine the relative body pitch angle θ_(Y2) commensurate with ameasured longitudinal acceleration A_(X). For example, a stored value ofpitch gradient can be referenced and multiplied by the measuredlongitudinal acceleration A_(X). The relative body pitch angle θ_(Y2) isoutput to the variable frequency high-pass signal filter 37.

The slip calculator 31 compares the wheel speed signals WS1-4 to thereference velocity V to determine the wheel slip for each wheel FL, FR,RL, RR. The confidence estimator 33 receives the calculated wheel slipfor each wheel FL, FR, RL, RR, along with the measured longitudinalacceleration A_(X), the brake pressure signal S2 and the throttle pedalposition signal S1. The confidence estimator 33 calculates a confidencevalue F in the calculated global pitch angle θ_(y). In the presentembodiment, the confidence value F lies in the range zero (0) to one(1), with zero (0) representing the maximum confidence and one (1)representing the minimum confidence. The confidence value F is used todetermine a filter coefficient F_(C) to set the cut-off frequency of thevariable frequency low-pass signal filter 35 and the cut-off frequencyof the variable frequency high-pass signal filter 37. The cut-offfrequency of the variable frequency low-pass signal filter 35 is set atthe same value as the cut-off frequency of the variable frequencyhigh-pass signal filter 37 to provide complementary signal filtering. Inthe present embodiment, the filter coefficient F_(C) is calculated bysubtracting the determined confidence value F from one (1). Thus, thesmaller the confidence value F (representing a higher confidence in thecalculated global pitch angle θ_(y)), the higher the cut-off frequencyof the variable frequency low-pass signal filter 35 and the variablefrequency high-pass signal filter 37. Conversely, the larger theconfidence value F (representing a lower confidence in the calculatedglobal pitch angle θ_(y)), the lower the cut-off frequency of thevariable frequency low-pass signal filter 35 and the variable frequencyhigh-pass signal filter 37. As illustrated in FIG. 6, the global pitchangle θ_(y) is filtered by the variable frequency low-pass signal filter35; and the relative body pitch angle θ_(Y2) is filtered by the variablefrequency high-pass signal filter 37. The processor 21 sums the filteredsignals to generate the filtered global pitch angle estimate θ_(YF).

The operation of the confidence estimator 33 will now be described inmore detail with reference to a block diagram 200 shown in FIG. 8.

The confidence estimator 33 receives the measured longitudinalacceleration A_(X) and determines a rate of change of the longitudinalacceleration A_(X) with respect to time (STEP 205), which can bereferred to as jerk. A first discrete high frequency filter (for example3-5 Hz) is applied to the rate of change signal (STEP 210) and themagnitude of the resultant signal determined (STEP 215). A first gain K1is then applied (STEP 220) to generate a first confidence value F1 whichprovides an indication of a confidence in the calculated global pitchangle θ_(y) based on the current rate of change in the longitudinalacceleration A_(X) of the vehicle 3. In the present embodiment, thefirst gain K1 is set at 0.08, but this value can be calibrated to suitparticular applications. The first confidence value F1 is output to acomparator 39.

The confidence estimator 33 receives the throttle pedal position signalS1 and determines a rate of change of the throttle pedal position withrespect to time (STEP 225). A second discrete high frequency filter (forexample 5 Hz) is applied to the rate of change signal (STEP 230) and themagnitude of the resultant signal determined (STEP 235). A second gainK2 is applied (STEP 240) to generate a second confidence value F1 whichprovides an indication of a confidence in the calculated global pitchangle θ_(y) based on the current rate of change of the throttle pedalposition. In the present embodiment, the second gain K1 is set at 0.003,but this value can be calibrated to suit particular applications. Thesecond confidence value F2 is output to the comparator 39.

The confidence estimator 33 receives the brake pressure signal S2 anddetermines the magnitude of the brake pressure (STEP 245). The brakepressure is compared to a look-up table (STEP 250) to generate a thirdconfidence value F3 which provides an indication of a confidence in thecalculated global pitch angle θ_(y) based on the current the brakepressure. The look-up table defines a dead band for brake pressuresbelow 50 bar. If the brake pressure is less than 50 bar, a value of zero(0) is returned as a third confidence value F3. If the brake pressure isgreater than 50 bar, a third gain K3 is applied to generate the thirdcandidate filter coefficient F3. In the present embodiment, the thirdgain K3 is interpolated linearly between 0 and 1 in dependence on brakepressure measurement between 50 bar and 100 bar. By way of example, thethird gain K3 is set as 1 when the brake pressure is greater than orequal to 100 bar, 0.5 when the brake pressure is 75 bar; and zero whenthe brake pressure is less than or equal to 50 bar. The third confidencevalue F3 provides an indication of a confidence in the calculated globalpitch angle θ_(y) based on the current brake pressure. It will beunderstood that the third gain K3 can be calibrated to suit particularapplications.

The slip calculator 31 receives the wheel speed signals WS1-4 from eachspeed sensor to determine the difference in the rotational speed of thefront and rear wheels on each side of the vehicle. A first slipcalculator 41 receives the wheel speed signals WS1, S3 for the wheelsFL, RL on the left hand side of the vehicle 3 and determines thedifference in their respective rotational speeds (STEP 255). The firstslip calculator 41 subtracts the rotational speed of the rear left wheelRL from the rotational speed of the front left wheel FL and outputs afirst slip value SL1. The first slip value SL1 is output to a firstlow-pass signal filter 43 which filters the first slip value SL1 (STEP260) and the first filtered slip value SL1F is output to a multiplexer45. The first filtered slip value SL1F is expressed as a percentage (%).

A second slip calculator 47 receives the wheel speed signals S2, S4 forthe wheels FR, RR on the right hand side of the vehicle 3 and determinesthe difference in their respective rotational speeds (STEP 265). Thesecond slip calculator 47 subtracts the rotational speed of the rearright wheel RL from the rotational speed of the front right wheel FR andoutputs a second slip value SL2. The second slip value SL2 is output toa second low-pass signal filter 49 which filters the second slip valueSL2 (STEP 270) and the second filtered slip value SL2F is output to themultiplexer 45. The second filtered slip value SL2F is expressed as apercentage (%).

The multiplexer 45 outputs an array comprising the first and secondfiltered slip values SL1F, SL2F (STEP 275). A fourth gain K4 is appliedto the array (STEP 280) to generate a fourth confidence value F4. Thefourth gain K4 is a non-linear relationship defined with reference to agraph in which the fourth gain K4 is defined along an X-axis (0, 0, 0.3.0.8, 0.9) and the wheel slip is defined along a Y-axis (0, 0.008, 0.01,0.015, 0.035). The fourth confidence value F4 provides an indication ofa confidence in the calculated global pitch angle θ_(y) based on thedetected wheel slip. By way of example, a detected wheel slip of 1%results in the fourth confidence value F4 being output as 0.3. Themaximum detected wheel slip SL1F, SL2F is compared to a predeterminedslip threshold (STEP 285), the slip threshold being set as 4.5% in thepresent embodiment. An uncertainty signal S5 is output to indicate aconfidence rating in the calculated global pitch angle θ_(y). Theuncertainty signal S5 is set to zero (0) if the detected wheel slipexceeds the slip threshold; and the uncertainty signal is set to one (1)if the detected wheel slip is less than the slip threshold.

The first, second, third and fourth gains K1-4 are operative tonormalize the first, second, third and fourth confidence values F1-4 toone (1), such that zero (0) represents the lowest confidence and one (1)represents the highest confidence. The comparator 39 selects the highestof the first, second, third and fourth confidence values F1-4 whichrepresents the lowest confidence in the calculated global pitch angleθ_(y) (STEP 290). The processor 21 subtracts the selected confidencevalue Fx from one (1) (STEP 295) and applies upper and lower saturationlimits (STEP 300). The upper and lower saturation limits are set as 0.01and 1 respectively. The resulting signal is multiplied by theuncertainty signal S5 (STEP 305) and a rising rate limit applied (STEP310). In the present embodiment, the rising rate limit is set to 0.7.The resulting signal is output (STEP 315) from the confidence estimator33 as a dynamic filter coefficient F_(C). The dynamic filter coefficientF_(C) sets the first cut-off frequency of the variable frequencylow-pass signal filter 35 and the second cut-off frequency of thevariable frequency high-pass signal filter 37.

The calculated global pitch angle θ_(Y) is filtered by the variablefrequency low-pass signal filter 35; and the relative body pitch angleθ_(Y2) is filtered by the variable frequency high-pass signal filter 37.The processor 21 sums the filtered signals to generate the filteredglobal pitch angle estimate θ_(YF) for output from the vehicle stateestimation apparatus 1. As described herein, the filtered global pitchangle estimate θ_(YF) can be used by vehicle dynamics controls.

The operation of the vehicle state estimation apparatus 1 to generatethe filtered global pitch angle estimate θ_(YF) will now be describedfor a first dynamic scenario in which the vehicle 3 undergoes heavybraking from a reference velocity of approximately 100 kph to 5 kph.FIG. 9A shows a first graph 400 showing a measured steering wheelangle)(°); FIG. 9B shows a second graph 410 showing a measured referencevelocity (U); and FIG. 9C shows a third graph 420 showing a measuredlateral acceleration (g). The steering wheel angle, reference velocityand the lateral acceleration are measured concurrently during a firsttime period. The dynamic filtering of the calculated global pitch angleθ_(Y) during the first time period is represented in a fourth graph 430shown in FIG. 10A; and the determined dynamic filter coefficient F_(C)during the first time period is represented in a fifth graph 440 shownin FIG. 10B. With reference to FIG. 10A, a first trace T1 shows thecalculated global pitch angle θ_(Y); a second trace T2 shows thefiltered global pitch angle estimate θ_(YF) generated when the dynamicfilter is applied to the calculated global pitch angle θ_(Y); a thirdtrace T3 shows a filtered global pitch angle θ_(Y) generated by applyinga discrete filter to the calculated global pitch angle θ_(Y); and afourth trace T4 shows a measured global pitch angle θ_(Y) for comparisonpurposes. The calculated global pitch angle θ_(Y) creates an erroneoussignal in periods of wheel slip during the braking event. However, bydynamically controlling the filter coefficient F_(C), the filteredglobal pitch angle estimate θ_(YF) more closely follows the measuredglobal pitch angle θ_(Y). This is evident from FIG. 10A in which thesecond trace T2 more closely follows the fourth trace T4 than either thefirst trace T1 or the third trace T3.

The operation of the vehicle state estimation apparatus 1 to generatethe filtered global pitch angle estimate θ_(YF) will now be describedfor a second dynamic scenario in which the vehicle 3 experiences excessroll as it travels around a hairpin corner. FIG. 11A shows a sixth graph450 showing a measured steering wheel angle)(°); FIG. 11B shows aseventh graph 460 showing a measured reference velocity (U); and FIG.11C shows an eighth graph 470 showing a measured lateral acceleration(g). The steering wheel angle, reference velocity and the lateralacceleration are measured concurrently during a second time period. Thedynamic filtering of the calculated global pitch angle θ_(Y) during thesecond time period is illustrated in a ninth graph 480 shown in FIG.12A; and the determined dynamic filter coefficient F_(C) during thefirst time period is shown in a tenth graph 490 FIG. 12B. With referenceto FIG. 11A, a first trace T1 shows the calculated global pitch angleθ_(Y); a second trace T2 shows the filtered global pitch angle estimateθ_(YF) generated when the dynamic filter is applied to the calculatedglobal pitch angle θ_(Y); a third trace T3 shows a filtered global pitchangle θ_(Y) generated by applying a discrete filter to the calculatedglobal pitch angle θ_(Y); and a fourth trace T4 shows a measured globalpitch angle θ_(Y) for comparison purposes. The calculated global pitchangle θ_(Y) creates an erroneous signal in periods of wheel slip duringthe braking event. However, by dynamically controlling the filtercoefficient F_(C), the filtered global pitch angle estimate θ_(YF) moreclosely follows the measured global pitch angle θ_(Y). This is evidentfrom FIG. 11A in which the second trace T2 more closely follows thefourth trace T4 than either the first trace T1 or the third trace T3.The filter coefficient F_(C) during the second time period is shown in afifth trace T5 in tenth graph 490 shown in FIG. 12B.

It will be appreciated that various changes and modifications can bemade to the vehicle state estimation apparatus 1 described herein. Thevehicle state estimation apparatus 1 could be configured to estimatebody roll angle θ_(X). For example, the vehicle state estimationapparatus 1 could use dynamic vehicle parameters such as lateralvelocity and/or lateral acceleration; and/or control inputs such assteering angle θ.

The vehicle state estimation apparatus 1 has been described withreference to determining the filtered global pitch angle estimateθ_(YF). However, it has been recognised that the techniques are alsoapplicable to determine the reference velocity V of the vehicle 2.Notably, the confidence estimator 33 can provide an indication of theconfidence in the reference velocity V. The dynamic filter coefficientF_(C) generated by the confidence estimator 33 can be used to set acut-off frequency of a variable frequency low-pass signal filter 35and/or a variable frequency high-pass signal filter 37. The referencevelocity V can be determined in dependence on the resulting filteredsignal(s). The reference velocity V is output to vehicle dynamiccontrollers and used to control dynamic operation of the vehicle 2. Bydetermining confidence in the calculated reference velocity V and/orimproving the accuracy of the reference velocity V, more robust vehiclecontrol can be achieved. The application of the global pitch angleestimate techniques to determine the reference velocity V of the vehicle2 will now be described with reference to FIGS. 13 to 18.

As shown in FIG. 13, the vehicle 3 is an automotive vehicle having fourwheels FL, FR, RL, RR, an inertial measurement unit (IMU) 5, a throttlepedal 7, a brake pedal 9 and a steering wheel 11. The IMU 5 comprises afirst accelerometer adapted to measure the longitudinal accelerationA_(X) of the vehicle 3 (i.e. acceleration along the longitudinal axisX); and a second accelerometer adapted to measure angular accelerationω_(Z) about the vertical axis Z. A rotational speed sensor 13 isprovided to measure the rotational speed of each wheel FL, FR, RL, RR togenerate wheel speed signals WS1-4. A first position sensor 15 isprovided to measure the position of the throttle pedal 7 and to output athrottle pedal position signal S1. A pressure sensor 17 is provided tomeasure the hydraulic pressure in the brake system and to output a brakepressure signal S2. A steering wheel angle sensor 19 is provided tomeasure the steering angle θ of the steering wheel 11 and to output asteering angle signal S3.

The rotational speed sensor 13 for each wheel FL, FR, RL, RR in thepresent embodiment is in the form of a magnetic (Hall effect) sensoroperative in combination with a coded toothed toning disc in theassociated wheel hub. The wheel speed is translated to a single datumpoint of the vehicle, for example to an assumed centre of gravity (CoG)of the vehicle 3. With reference to FIG. 14, the longitudinal wheelspeed V of each wheel FL, FR, RL, RR, translated to the COG, iscalculated using the following equations:

$V_{{FL},{CoG}} = {\frac{V_{FL}}{\cos\;\theta} + {{1/2}\; T\;\omega_{z}}}$$V_{{FR},{CoG}} = {\frac{V_{FR}}{\cos\;\theta} - {{1/2}\; T\;\omega_{z}}}$V_(RL, CoG) = V_(RL) + 1/2 T ω_(z) V_(RR, CoG) = V_(RR) − 1/2 T ω_(z)Where: V_(CoG) is the wheel speed translated to the CoG;

-   -   V is the measured speed of each wheel (FL, FR, RL, RR);    -   θ is the steering angle;    -   ω_(Z) is the angular acceleration about the vertical axis Z; and    -   T is the wheel track.

As shown in FIG. 15, the measured wheel speeds V_(FL), V_(FR), V_(RL),V_(RR), the steering angle θ, the wheel track T, the angularacceleration ω_(Z) and the longitudinal acceleration A_(X) are output toa wheel speed translator 53 configured to calculate the translatedlongitudinal wheel speed V_(CoG) of each wheel FL, FR, RL, RR. Thetranslated longitudinal wheel speeds V_(CoG) are output to the referencevelocity estimator 55 which outputs the longitudinal reference velocityV for the vehicle 3.

The reference velocity V of the vehicle 3 can be determined by averagingthe measured speed of each wheel FL, FR, RL, RR. The reference velocityestimator 55 can optionally perform one or more of the followingfunctions:

-   -   (a) Remove effects of steering angle θ and/or yaw angle using        measured vehicle parameters from the steering wheel 11 and/or        dynamic parameters measured by the on board IMU 5.    -   (b) In a two-wheel drive application, the forward velocity can        be determined based on the measured speed of the non-driven        wheels (since these are less likely to be in positive slip from        positive engine torque).    -   (c) In high lateral acceleration maneuvers, the forward velocity        can be determined based on the average of the rotational speeds        of the outside wheels (since these are less likely to lose        traction with the road surface, for example due to lifting).    -   (d) Using longitudinal acceleration A_(X) from the IMU 5 to        perform plausibility checks on wheel speed information, for        example if the vehicle 3 is not decelerating but wheels speeds        are very low this can imply a wheel lock scenario (and one or        more measured wheel speeds can be ignored).    -   (e) Integration of longitudinal acceleration for short periods        of time when all wheel speeds are determined unstable.

A slip calculator 31 is provided for calculating wheel slip valuesSL1-4, as shown in FIG. 16. The slip calculator 31 uses the measuredwheel speeds V_(FL), V_(FR), V_(RL), V_(RR) from the speed sensors 13 tocalculate the wheel slip values, as described herein with reference tothe vehicle state estimation apparatus 1. A reference velocityconfidence estimator 157 is provided to calculate one or more confidencevalues F1 in dependence on at least one vehicle dynamic parameter and/orat least one control input. The calculated confidence value F1 providesan indication of the confidence in the calculated reference velocity V.In the present embodiment the confidence value F1 ranges from 0 to 1(inclusive), where 0 represents low confidence and 1 represents highconfidence. The reference velocity confidence estimator 157 receives atleast one vehicle dynamic parameter such as the wheel slip values SL1-4determined by the slip calculator 31 and/or longitudinal accelerationmeasured by the IMU 5; and at least one control input, such as thethrottle pedal signal S1 and/or the brake pressure signal S2. Thereference velocity confidence estimator 157 can optionally also receivethe measured wheel speeds V_(FL), V_(FR), V_(RL), V_(RR), the steeringangle θ, the wheel track T, the angular acceleration ω_(Z) and thelongitudinal acceleration A_(X) from the corresponding sensors. Theconfidence value F1 is output to the reference velocity estimator 55and/or to a dynamic controller 59. The reference velocity estimator 55determines the vehicle reference velocity V in dependence on theconfidence value F1, for example by applying a dynamic filter.

An overview of the operation of the reference velocity confidenceestimator 57 is provided in a flow diagram 500 shown in FIG. 17. Thereference velocity confidence estimator 57 receives the wheel speedsV_(FL), V_(FR), V_(RL), V_(RR), (STEP 505), which can be filtered orunfiltered (raw) data. A first estimation of the reference velocity V iscalculated (STEP 510) using the wheel speeds V_(FL), V_(FR), V_(RL),V_(RR). A variable frequency filter is applied to the calculatedreference velocity V (STEP 515) to remove noise or erroneous overshoots.An operating frequency of the variable frequency filter is determined independence on the calculated confidence value F1 (or a selected one of aplurality of said calculated confidence values F1). Specifically, thecalculated confidence value F1 is used to determine a filter coefficientF_(C) to control an operating frequency of the variable frequencyfilter. In the present embodiment, the confidence value F1 is calculatedin dependence on a control input in the form of the throttle pedalposition signal S1 and/or the brake pressure signal S2 (STEP 520). Thereference velocity confidence estimator 57 receives the at least onecontrol input (STEP 525) and performs a check to determine if thecontrol inputs are significant (STEP 530). For example, the referencevelocity confidence estimator 57 can determine if the throttle pedalposition signal S1 and the brake pressure signal S2 exceed respectivethresholds. If the control inputs are determined not to be significant,no action is required (STEP 535). If, however, the control inputs aredetermined to be significant, the confidence value F1 is modifieddynamically. The determined confidence value F1 sets the operatingfrequency of the variable frequency filter which is then used to filterthe calculated reference velocity V (STEP 540). A filtered referencevelocity V_(F) is output (STEP 535), for example to a vehicle dynamicscontroller to control dynamic operation of the vehicle 3.

In a similar manner to the dynamic filtering of global and relativepitch described herein, the determined confidence value F1 can be usedto calculate a filtered reference velocity V_(F) from multiple referencevelocity sources. By generating the filtered reference velocity fromseveral different sources, a more accurate estimate of the referencevelocity V can be obtained. A first reference velocity V₁ can be derivedfrom the measured speed of the wheels FL, FR, RL, RR; and a secondreference velocity V₂ can be derived from a second source, such asintegration of longitudinal acceleration A_(X) from the IMU 5, the speedobtained from GPS information, or another source. The first and secondreference velocities V₁, V₂ can be dynamically filtered in dependence onthe determined confidence value F1 and then combined to generate thefiltered reference velocity V_(F). The two filtered signals cancomplement each other to cover the whole desired frequency range.Indeed, at least in certain embodiments, there may be a third oradditional source(s) of reference velocity V and a three way or morecombination of signals made. The calculation of a filtered referencevelocity V_(F) from multiple sources will now be described.

The determination of the filtered reference velocity V_(F) from firstand second reference velocities V₁, V₂ will now be described withreference to in a flow diagram 600 shown in FIG. 18. The first referencevelocity V₁ is obtained from a first source which in the presentembodiment is the reference velocity calculator 25 which receives thewheel speed signals WS1-4 from the rotational speed sensors 13associated with each wheel FL, FR, RL, RR. The first reference velocityV₁ is calculated using the wheel speed signals WS1-4 to determine themean rotational speed WS of the wheels FL, FR, RL, RR (STEP 605). Thesecond reference velocity V₂ is obtained from a second source which inthe present embodiment is the longitudinal acceleration A_(X) measuredby the IMU 5 (STEP 610). Alternatively, or in addition, the secondsource could comprise global positioning system (GPS) operative tomeasure the second reference velocity V₂.

The first reference velocity V₁ is output to the variable frequencylow-pass filter 35; and the second reference velocity V₂ is output tothe variable frequency high-pass filter 37. A cut-off frequency of thevariable frequency low-pass signal filter 35 can be set at between zero(0) and one (1) Hertz inclusive. Similarly, the cut-off frequency of thevariable frequency high-pass signal filter 37 can be set between zero(0) and one (1) Hertz inclusive. As described herein, the referencevelocity confidence estimator 57 calculates the confidence value F1 independence on at least one vehicle dynamic parameter and/or at least onecontrol input. In the present embodiment, the confidence value F lies inthe range zero (0) to one (1), with zero (0) representing the maximumconfidence and one (1) representing the minimum confidence. Theconfidence value F is used to determine a filter coefficient F_(C) toset the cut-off frequency of the variable frequency low-pass signalfilter 35 and the cut-off frequency of the variable frequency high-passsignal filter 37. The cut-off frequency of the variable frequencylow-pass signal filter 35 and the variable frequency high-pass signalfilter 37 are set at the same value in dependence on the determinedfilter coefficient F_(C). As illustrated in FIG. 18, the first referencevelocity V₁ is filtered by the variable frequency low-pass filter 35(STEP 615); and the second reference velocity V₂ is filtered by thevariable frequency high-pass filter 37 (STEP 620). The filtered firstand second signals are then summed (STEP 625) to generate the filteredglobal reference velocity V_(F). The filtered global reference velocityV_(F) is then output (STEP 630).

Alternatively, or in addition, the reference velocity confidenceestimator 57 can be output to a vehicle dynamic controller 61. The useof the reference velocity confidence estimator 57 to control the vehicledynamic controller 61 is illustrated in a flow diagram 700 shown in FIG.19. A reference longitudinal velocity V_(x) (STEP 705) and a referencetransverse velocity V_(y) (STEP 710) are input to a side slip estimator163 which estimates a side slip angle β at the rear axle using theequation β=V_(y)/V_(x) (STEP 715). A side slip angle β (STEP 720) and aside slip rate {dot over (β)} (STEP 725) are output to a proportionalderivative (PD) side slip controller 163 (STEP 730). The referencevelocity confidence estimator 157 determines the confidence value F1 forthe calculated longitudinal reference velocity V_(x) and/or thecalculated transverse reference velocity V_(Y) and this is also outputto the PD side slip controller 163 (STEP 735). The PD side slipcontroller 163 outputs a control signal to the vehicle dynamiccontroller 61 (STEP 740). The operation of the vehicle dynamiccontroller 61 is controlled in dependence on the control signal. By wayof example, if the confidence value F1 output to the PD side slipcontroller 163 is low, this can provide an indication that thecalculated side slip angle β is smaller than or larger than wasintended. The PD side slip controller 163 is configured to output acontrol signal to the vehicle dynamic control to deliver brake pressuresto the wheels to generate a yaw torque. The PD side slip controller 61can be tuned to achieve desired levels of vehicle slip angle or ratemagnitude. In the event that the reference velocity confidence estimator157 determining a low confidence in the calculated reference velocity V,the PD side slip controller 163 could be configured to turn off the PDside slip controller 163, as response cannot be relied upon.Alternatively, or in addition, the PD side slip controller 163 can beconfigured to change the vehicle dynamic controller 61 to an alternateset of tuneable parameters, for example switch to a “sensitised” controlsetting having tighter dead bands and/or gains in order to capture sideslip events at lower input levels. At least in certain embodiments, thisapproach would be appropriate since the calculated reference velocity Vmight be greater than the real value.

It will be appreciated that various changes and modifications can bemade to the apparatus and methods described herein without departingfrom the scope of the present application.

Further aspects of the present invention are set out in the followingnumbered paragraphs:

1. An apparatus for estimation of a vehicle state, the apparatuscomprising

-   -   a controller comprising an electronic processor having an        electrical input for receiving at least one first vehicle        dynamics parameter signal and a least a first vehicle operating        parameter signal;    -   an electronic memory device electrically coupled to the        electronic processor and having instructions stored therein,    -   wherein the electronic processor is configured to access the        memory device and execute the instructions stored therein such        that it is operable to:        -   determine a first estimation of the vehicle state in            dependence on at least one first vehicle dynamics parameter;        -   determine a filter coefficient in dependence on a first            vehicle operating parameter; and        -   set an operating frequency of a first signal filter in            dependence on the determined filter coefficient and use the            first signal filter to filter the first estimation to            generate a first filtered estimation of the vehicle state;            and        -   output a control signal in dependence on the first filtered            estimation of the vehicle state.            2. An apparatus as described in paragraph 1, wherein the            vehicle state is a pitch angle of the vehicle measured about            a transverse axis; and the at least one first vehicle            dynamics parameter comprises a reference velocity along a            longitudinal axis of the vehicle.            3. An apparatus as described in paragraph 1, wherein said            first signal filter is a low-pass signal filter and the            operating frequency of the first signal filter is a cut-off            frequency of the low-pass signal filter.            4. Apparatus as described in paragraph 1, wherein the at            least one first vehicle dynamics parameter is one or more            parameters selected from the following set: reference            velocity, longitudinal velocity, longitudinal acceleration,            lateral velocity, lateral acceleration, vertical velocity,            vertical acceleration, roll, yaw, pitch and wheel slip.            5. An apparatus as described in paragraph 1, wherein the            electronic processor is configured to access the memory            device and execute the instructions stored therein such that            it is operable to:    -   determine a second estimation of the vehicle state in dependence        on at least one second vehicle dynamics parameter;    -   set an operating frequency of a second signal filter in        dependence on the determined filter coefficient and use the        second signal filter to filter the second estimation to generate        a second filtered estimation of the vehicle state.        6. An apparatus as described in paragraph 5, wherein the second        estimation is determined by referencing the at least one second        vehicle dynamics parameter to a look-up table stored in system        memory.        7. Apparatus as described in paragraph 5, wherein the at least        one second vehicle dynamics parameter is one or more parameters        selected from the following set: reference velocity,        longitudinal velocity, longitudinal acceleration, lateral        velocity, lateral acceleration, vertical velocity, vertical        acceleration, roll, yaw, pitch, and wheel slip.        8. An apparatus as described in paragraph 5, wherein said second        signal filter is a high-pass signal filter, and the operating        frequency of the second signal filter is a cut-off frequency of        the high-pass signal filter.        9. An apparatus as described in paragraph 5, wherein the        electronic processor is operable to combine the first and second        filtered estimations.        10. An apparatus as described in paragraph 5, wherein the second        estimation of the vehicle state determines a relative body pitch        angle of the vehicle.        11. An apparatus as described in paragraph 1, wherein the        wherein the electronic processor is operable to access the        memory device and execute the instructions stored therein such        that it is operable to:    -   generate a confidence value of the first estimation in        dependence on the first vehicle operating parameter; and    -   the filter coefficient is calculated based on said confidence        value.        12. An apparatus as described in paragraph 11, wherein said        first vehicle operating parameter comprises longitudinal vehicle        acceleration; and the electronic processor is operable to        determine a rate of change of the longitudinal vehicle        acceleration and to generate a first confidence value of the        first estimation in dependence on said determined rate of change        of the longitudinal vehicle acceleration.        13. An apparatus as described in paragraph 12, wherein the        electronic processor is operable to apply a high frequency        filter to the determined rate of change of the longitudinal        vehicle acceleration.        14. An apparatus as described in paragraph 11, wherein said        first vehicle operating parameter comprises a throttle pedal        position; and the electronic processor is operable to determine        a rate of change of the throttle pedal position and to generate        a second confidence value of the first estimation in dependence        on the determined rate of change of the throttle pedal position.        15. An apparatus as described in paragraph 14, wherein the        electronic processor is operable to apply a high frequency        filter to the determined rate of change of the throttle pedal        position.        16. An apparatus as described in paragraph 11, wherein said        first vehicle operating parameter comprises brake pressure; and        wherein the electronic processor is operable to generate a third        confidence value of the first estimation in dependence on the        brake pressure.        17. An apparatus as described in paragraph 11, wherein said        first vehicle operating parameter comprises at least one wheel        slip measurement; and wherein the electronic processor is        operable to generate a fourth confidence value of the first        estimation in dependence on the at least one wheel slip        measurement.        18. An apparatus as described in paragraph 17, wherein the at        least one wheel slip measurement is compared to a look-up table        to generate the fourth confidence value.        19. An apparatus as described in paragraph 17, wherein the        analysis of the wheel slip measurement comprises comparing first        and second wheel slip measurements to a look-up table.        20. An apparatus as described in paragraph 11, wherein the        filter coefficient is determined in dependence on the generated        confidence value.        21. An apparatus as described in paragraph 11, wherein the        electronic processor is operable to generate a plurality of said        confidence values, each confidence value being generated in        dependence on a different first operating parameter; and wherein        the controller is operable to generate the filter coefficient in        dependence on the generated confidence value indicating the        lowest confidence in the first estimation.        22. An apparatus as described in paragraph 20, wherein the        electronic processor is operable to invert the generated        confidence value, the filter coefficient being generated in        dependence on the inverted confidence value.        23. A dynamic filtering apparatus comprising:    -   a controller comprising an electronic processor having an        electrical input for receiving at least one first vehicle        dynamics parameter signal and a least a first vehicle operating        parameter signal;    -   an electronic memory device electrically coupled to the        electronic processor and having instructions stored therein,    -   wherein the electronic processor is configured to access the        memory device and execute the instructions stored therein such        that it is operable to:        -   generate a first signal and a second signal;        -   calculate a cut-off frequency;        -   apply the calculated cut-off frequency to a low-pass signal            filter and filter the first signal using the low-pass signal            filter;        -   apply the calculated cut-off frequency to a high-pass signal            filter and filter the second signal using the high-pass            signal filter; and        -   combine the filtered outputs of said low-pass signal filter            and said high-pass signal filter.            24. A dynamic filtering apparatus as described in paragraph            23, wherein the electronic processor is operable to generate            the first signal in dependence on at least one first            parameter.            25. A dynamic filtering apparatus as described in paragraph            24, wherein the at least one first parameter is at least one            first vehicle dynamics parameter selected from the following            set: reference velocity, longitudinal velocity, longitudinal            acceleration, lateral velocity, lateral acceleration,            vertical velocity, vertical acceleration, roll, yaw, pitch            and wheel slip.            26. A dynamic filtering apparatus as described in paragraph            23, wherein the electronic processor is operable to generate            the second signal in dependence on at least one second            parameter.            27. A dynamic filtering apparatus as claimed in claim 26,            wherein the at least one second parameter is at least one            second vehicle dynamics parameter selected from the            following set: reference velocity, longitudinal velocity,            longitudinal acceleration, lateral velocity, lateral            acceleration, vertical velocity, vertical acceleration,            roll, yaw, pitch, and wheel slip.            28. A dynamic filtering apparatus as described in paragraph            23, wherein the electronic processor is operable to            calculate a first confidence value of the first signal; and            the cut-off frequency is calculated in dependence on said            first confidence value.            29. A dynamic filtering apparatus as described in paragraph            28, wherein the first confidence value is calculated in            dependence on a third parameter.            30. A dynamic filtering apparatus as described in paragraph            29, wherein the third parameter is a vehicle dynamics            parameter or a vehicle control input.            31. A vehicle comprising apparatus as described in paragraph            1.            32. A method of estimating a vehicle state, the method            comprising:    -   determining a first estimation of the vehicle state in        dependence on at least one first vehicle dynamics parameter;    -   determining a filter coefficient in dependence on a first        vehicle operating parameter;    -   setting an operating frequency of a first signal filter in        dependence on the determined filter coefficient and using the        first signal filter to filter the first estimation to generate a        first filtered estimation of the vehicle state; and    -   outputting a control signal in dependence on the first filtered        estimation of the vehicle state.        33. A method as described in paragraph 32, wherein the vehicle        state is a pitch angle of the vehicle measured about a        transverse axis; and the at least one first vehicle dynamics        parameter comprises a reference velocity along a longitudinal        axis of the vehicle.        34. A method as described in paragraph 32, wherein said first        signal filter is a low-pass signal filter and the operating        frequency of the first signal filter is a cut-off frequency of        the low-pass signal filter.        35. A method as described in paragraph 32, wherein the at least        one first vehicle dynamics parameter is one or more parameters        selected from the following set: reference velocity,        longitudinal velocity, longitudinal acceleration, lateral        velocity, lateral acceleration, vertical velocity, vertical        acceleration, roll, yaw, pitch and wheel slip.        36. A method as described in paragraph 32 comprising:    -   determining a second estimation of the vehicle state in        dependence on at least one second vehicle dynamics parameter;    -   setting an operating frequency of a second signal filter in        dependence on the determined filter coefficient and using the        second signal filter to filter the second estimation to generate        a second filtered estimation of the vehicle state.        37. A method as described in paragraph 36 comprising determining        the second estimation by referencing the at least one second        vehicle dynamics parameter to a look-up table stored in system        memory.        38. A method as described in paragraph 36, wherein the at least        one second vehicle dynamics parameter is one or more parameters        selected from the following set: reference velocity,        longitudinal velocity, longitudinal acceleration, lateral        velocity, lateral acceleration, vertical velocity, vertical        acceleration, roll, yaw, pitch, and wheel slip.        39. A method as described in paragraph 36, wherein said second        signal filter is a high-pass signal filter, and the operating        frequency of the second signal filter is a cut-off frequency of        the high-pass signal filter.        40. A method as described in paragraph 36 comprising combining        the first and second filtered estimations.        41. A method as described in paragraph 36, wherein the second        estimation of the vehicle state determines a relative body pitch        angle of the vehicle.        42. A method as described in paragraph 32 comprising generating        a confidence value of the first estimation in dependence on a        first vehicle operating parameter; and calculating the filter        coefficient based on said confidence value.        43. A method as described in paragraph 42, wherein said first        vehicle operating parameter comprises longitudinal vehicle        acceleration; and the method comprises determining a rate of        change of the longitudinal vehicle acceleration, and generating        a first confidence value of the first estimation in dependence        on said determined rate of change of the longitudinal vehicle        acceleration.        44. A method as described in paragraph 43 comprising applying a        high frequency filter to the determined rate of change of the        longitudinal vehicle acceleration.        45. A method as described in paragraph 42, wherein said first        vehicle operating parameter comprises a throttle pedal position;        and the method comprises determining a rate of change of the        throttle pedal position to generate a second confidence value of        the first estimation.        46. A method as described in paragraph 45 comprising applying a        high frequency filter to the determined rate of change of the        throttle pedal position.        47. A method as described in paragraph 42, wherein said first        vehicle operating parameter comprises brake pressure; and the        method comprises analysing the brake pressure to generate a        third confidence value of the first estimation.        48. A method as described in paragraph 42, wherein said first        vehicle operating parameter comprises at least one wheel slip        measurement; and the method comprises analysing the at least one        wheel slip measurement to generate a fourth confidence value of        the first estimation.        49. A method as described in paragraph 48 comprising comparing        the at least one wheel slip measurement to a look-up table to        generate the fourth confidence value.        50. A method as described in paragraph 48, wherein the analysis        of the wheel slip measurement comprises comparing first and        second wheel slip measurements to a look-up table.        51. A method as described in paragraph 42 comprising determining        the filter coefficient in dependence on the generated confidence        value or on one of the generated confidence values.        52. A method as described in paragraph 51 comprising generating        a plurality of said confidence values, each confidence value        being generated in dependence on a different first operating        parameter; the method comprising generating the filter        coefficient in dependence on the generated confidence value        indicating the lowest confidence in the first estimation.        53. A method as described in paragraph 51 comprising inverting        the generated confidence value, the filter coefficient being        generated in dependence on the inverted confidence value.        54. A dynamic filtering method comprising:    -   generating a first signal and a second signal;    -   calculating a cut-off frequency;    -   applying the calculated cut-off frequency to a low-pass signal        filter and filtering the first signal using the low-pass signal        filter;    -   applying the calculated cut-off frequency to a high-pass signal        filter and filtering the second signal using the high-pass        signal filter; and    -   combining the filtered outputs of said low-pass signal filter        and said high-pass signal filter.        55. A dynamic filtering method as described in paragraph 54        comprising generating the first signal in dependence on at least        one first parameter.        56. A dynamic filtering method as claimed in claim 55, wherein        the at least one first parameter is at least one first vehicle        dynamics parameter selected from the following set: reference        velocity, longitudinal velocity, longitudinal acceleration,        lateral velocity, lateral acceleration, vertical velocity,        vertical acceleration, roll, yaw, pitch and wheel slip.        57. A dynamic filtering method as described in paragraph 54        comprising generating the second signal in dependence on at        least one second parameter.        58. A dynamic filtering method as claimed in claim 57, wherein        the at least one second parameter is at least one second vehicle        dynamics parameter selected from the following set: reference        velocity, longitudinal velocity, longitudinal acceleration,        lateral velocity, lateral acceleration, vertical velocity,        vertical acceleration, roll, yaw, pitch, and wheel slip.        59. A dynamic filtering method as described in paragraph 54        comprising calculating a first confidence value of the first        signal; and calculating the cut-off frequency in dependence on        said first confidence value.        60. A dynamic filtering method as described in paragraph 54        comprising calculating the first confidence value in dependence        on a third parameter.        61. A dynamic filtering method as claimed in claim 60, wherein        the third parameter is a vehicle dynamics parameter or a vehicle        control input.

The invention claimed is:
 1. An apparatus for estimation of a vehiclestate, the apparatus comprising a controller comprising an electronicprocessor having an electrical input for receiving vehicle dynamicsparameter signals and vehicle operating parameter signals; an electronicmemory device electrically coupled to the electronic processor andhaving instructions stored therein, wherein the electronic processor isconfigured to access the memory device and execute the instructionsstored therein such that it is operable to: determine a first estimationof the vehicle state in dependence on at least one first vehicledynamics parameter; determine a filter coefficient in dependence on afirst vehicle operating parameter; set an operating frequency of a firstsignal filter in dependence on the determined filter coefficient and usethe first signal filter to filter the first estimation of the vehiclestate to generate a first filtered estimation of the vehicle state; andoutput a control signal to a vehicle dynamics controller in dependenceon the first filtered estimation of the vehicle state, wherein thevehicle state is a pitch angle of the vehicle measured about atransverse axis, and wherein the at least one first vehicle dynamicsparameter comprises a reference velocity along a longitudinal axis ofthe vehicle.
 2. The apparatus as claimed in claim 1, wherein the firstsignal filter is a low-pass signal filter and the operating frequency ofthe first signal filter is a cut-off frequency of the low-pass signalfilter.
 3. The apparatus as claimed in claim 1, wherein the at least onefirst vehicle dynamics parameter is one or more parameters selected fromthe following set: longitudinal velocity, longitudinal acceleration,lateral velocity, lateral acceleration, vertical velocity, verticalacceleration, roll, yaw, pitch and wheel slip.
 4. The apparatus asclaimed in claim 1, wherein the electronic processor is furtherconfigured to access the memory device and execute the instructionsstored therein to: determine a second estimation of the vehicle state independence on at least one second vehicle dynamics parameter; and set anoperating frequency of a second signal filter in dependence on thedetermined filter coefficient and use the second signal filter to filterthe second estimation of the vehicle state to generate a second filteredestimation of the vehicle state.
 5. The apparatus as claimed in claim 4,wherein the second estimation of the vehicle state is determined byreferencing the at least one second vehicle dynamics parameter to alook-up table stored in system memory.
 6. The apparatus as claimed inclaim 4, wherein the at least one second vehicle dynamics parameter isone or more parameters selected from the following set: referencevelocity, longitudinal velocity, longitudinal acceleration, lateralvelocity, lateral acceleration, vertical velocity, verticalacceleration, roll, yaw, pitch, and wheel slip.
 7. The apparatus asclaimed in claim 4, wherein the second signal filter is a high-passsignal filter, and the operating frequency of the second signal filteris a cut-off frequency of the high-pass signal filter.
 8. The apparatusas claimed in claim 4, wherein the electronic processor is operable tocombine the first and second filtered estimations of the vehicle state.9. The apparatus as claimed in claim 4, wherein the second estimation ofthe vehicle state determines a relative body pitch angle of the vehicle.10. A vehicle comprising the apparatus as claimed in claim
 1. 11. Adynamic filtering apparatus comprising: a controller comprising anelectronic processor having an electrical input for receiving vehicledynamics parameter signals and vehicle operating parameter signals; anelectronic memory device electrically coupled to the electronicprocessor and having instructions stored therein, wherein the electronicprocessor is configured to access the memory device and execute theinstructions stored therein such that it is operable to: generate afirst signal and a second signal; calculate a cut-off frequency independence on at least one vehicle dynamics parameter and/or at leastone control input; apply the calculated cut-off frequency to a low-passsignal filter and filter the first signal using the low-pass signalfilter; apply the calculated cut-off frequency to a high-pass signalfilter and filter the second signal using the high-pass signal filter;combine filtered outputs of the low-pass signal filter and the high-passsignal filter; and output a control signal to a vehicle dynamicscontroller in dependence on the combined filtered outputs of thelow-pass signal filter and the high-pass signal filter.
 12. The dynamicfiltering apparatus as claimed in claim 11, wherein the electronicprocessor is operable to generate the first signal in dependence on atleast one first parameter.
 13. The dynamic filtering apparatus asclaimed in claim 12, wherein the at least one first parameter is atleast one first vehicle dynamics parameter selected from the followingset: reference velocity, longitudinal velocity, longitudinalacceleration, lateral velocity, lateral acceleration, vertical velocity,vertical acceleration, roll, yaw, pitch and wheel slip.
 14. The dynamicfiltering apparatus as claimed in claim 11, wherein the electronicprocessor is operable to generate the second signal in dependence on atleast one second parameter.
 15. The dynamic filtering apparatus asclaimed in claim 14, wherein the at least one second parameter is atleast one second vehicle dynamics parameter selected from the followingset: reference velocity, longitudinal velocity, longitudinalacceleration, lateral velocity, lateral acceleration, vertical velocity,vertical acceleration, roll, yaw, pitch, and wheel slip.
 16. The dynamicfiltering apparatus as claimed in claim 11, wherein the electronicprocessor is operable to calculate a first confidence value of the firstsignal, and wherein the cut-off frequency is calculated in dependence onthe first confidence value.
 17. The dynamic filtering apparatus asclaimed in claim 16, wherein the first confidence value is calculated independence on a third parameter.
 18. The dynamic filtering apparatus asclaimed in claim 17, wherein the third parameter is a vehicle dynamicsparameter or a vehicle control input.
 19. A method of estimating avehicle state performed by an electronic processor having an electricalinput for receiving vehicle dynamics parameter signals and vehicleoperating parameter signals, the method comprising: determining a firstestimation of the vehicle state in dependence on at least one firstvehicle dynamics parameter; determining a filter coefficient independence on a first vehicle operating parameter; setting an operatingfrequency of a first signal filter in dependence on the determinedfilter coefficient and using the first signal filter to filter the firstestimation of the vehicle state to generate a first filtered estimationof the vehicle state; and outputting a control signal to a vehicledynamics controller in dependence on the first filtered estimation ofthe vehicle state, wherein the vehicle state is a pitch angle of thevehicle measured about a transverse axis, and wherein the at least onefirst vehicle dynamics parameter comprises a reference velocity along alongitudinal axis of the vehicle.
 20. A dynamic filtering methodperformed by an electronic processor having an electrical input forreceiving vehicle dynamics parameter signals and vehicle operatingparameter signals, the method comprising: generating a first signal anda second signal; calculating a cut-off frequency in dependence on atleast one vehicle dynamics parameter and/or at least one control input;applying the calculated cut-off frequency to a low-pass signal filterand filtering the first signal using the low-pass signal filter;applying the calculated cut-off frequency to a high-pass signal filterand filtering the second signal using the high-pass signal filter;combining filtered outputs of the low-pass signal filter and thehigh-pass signal filter; and outputting a control signal to a vehicledynamics controller in dependence on the combined filtered outputs ofthe low-pass signal filter and the high-pass signal filter.